
Class-specific attention learning with CASL: The CASL (co-activity similarity loss) was initially introduced in W-TALC[1]} and then inspired others Deep Metric [2]}, Action Graphs [3]}, WOAD [4]}, Actionbytes [5]}. The main idea is that for a pair of videos including the same action classes, the foreground features in both videos should be more similar than the foreground feature in one video and the background feature in the other video. For a pair of videos with indices \(m\)  and \(n\)  that include action class \(c\) , the foreground features are denoted by \(f^m_c\) , \(f^n_c\)  and the background features by \(b^m_c\) , \(b^n_c\)  (def REF ). Then CASL is defined based on ranking hinge loss as the following:
\(\begin{split}L^{mn}_c & = \frac{1}{2} \lbrace  \max \big (0,d(f^m_c , f^n_c)-d(f^m_c , b^n_c)+\delta \big ) \\& + \max \big (0,d(f^m_c , f^n_c)-d(b^m_c , f^n_c)+\delta \big ) \rbrace ,\end{split}\) 
